There is a growing focus among marketers to leverage customer transactional and behavioral data that is already available within an enterprise through advanced analytics. Micro-segmentation, acquisition models, lead qualification models, identifying cross-selling and up-selling opportunities through market basket analysis are all tools available to make the marketing task more focused.

However, and this is a big however, most of these tools are predictive tools based on past behavior. Reasons for a particular behavior can, at best, be inferred ie. we assume that consumers will behave in a certain way in future because of the way they have acted in the past. The big fly in the ointment is  that we do not know why they did so or why  they would do so in the future. This, to me, is incomplete understanding – because circumstances that drove that behavior may change or marketing initiatives to drive a particular behavior may be wrongfully directed. Which is why, there is a crying need to integrate attitudinal research into the behavioral analytics model.

Let’s say a supermarket has data to show that Hot Wheels cars and candy are purchased together. The easiest decision here is one of placement – place the 2 categories together for maximum uptake of both categories.  But do we need to stop there? Let’s say, the store was running a promotion targeted at kids. Should we place the promotion at this point – hypothesizing that the kids’ pester power was driving sales here? Or should we assume that the buyer is a young mom shopping alone - in which case, a promotion targeted at her is more likely to catch attention.  Questions like these can and should be answered with simple, on-ground research surveys.

To carry the same analogy further, data could throw up a high correlation between purchase of Hot Wheels and contraceptives. Obviously, common sense dictates that the placement decision is irrelevant here. But how could this information be leveraged for better offtake? Researching habits and attitudes even in a qualitative way armed with this information could provide significant insights. Very little granularity would derive from mere data mining.

The reverse is also true. A customer satisfaction survey would certainly provide inputs into evaluation and correction of internal and external processes.  But over time, smart businesses would find it much more optimal to link the feedback mechanism to customer value/business transactions so that they can act on the critical processes and not necessarily only the ones with the highest visibility.

While traditional market research as defined by the 45 minute, pen & paper interview is certainly becoming redundant in the pace of today’s world, the explosion of alternative media and technology has thrown up a whole clutch of other touchpoints for marketers and researchers to structure smart research around. Building a marketing recommendation around a strong foundation of attitudinal research led analytics will make the difference in the future.